Skip to main content

Actinobacterial Strains as Genomic Candidates for Characterization of Genes Encoding Enzymes in Bioconversion of Lignocellulose

Abstract

Many soil Actinobacteria are potent producers of extracellular enzymes decomposing lignocellulose. Four strains of Actinobacteria with a high potential to hydrolyse cellulose and hemicellulose were identified among environmental isolates. The strains were grown on raw lignocellulosic substrates (olive pomace, oat flakes, sawdust, and wheat straw) under submerged fermentation in a laboratory scale. Modified Melin Norkrans Medium amended with raw lignocellulosic substrates as carbon sources (0.5%) was used to enhance lignocellulosic biomass decomposition. Three strains belonged to the genus Streptomyces and one strain to the genus Mycobacterium. Annotation of genomes showed high proportion of genes encoding for carbohydrate-active enzymes in Streptomyces sp. GESEQ-4 (537, i.e. 6% of 8404 genes), Streptomyces sp. GESEQ-13 (351 (5%) of 6705 genes), Streptomyces sp. GESEQ-35 (608 (6%) of 9788 genes), and Mycolicibacterium fortuitum subsp. fortuitum GESEQ-9 (222 (3%) of 6405 genes). These included plant cell wall-degrading enzymes belonging to the families GH1, GH2, GH3, GH5, GH6, GH9, GH10, GH12, GH16, GH26, GH30, GH39, GH48, GH51, and GH74, of which GH1, GH2, GH3, GH5, GH6, and GH16 were found in all four genomes. Assays for cellulose and hemicellulose degrading extracellular enzymes confirmed the ability of the isolates to decompose cellulose and hemicellulose. The highest endo-cleaving enzyme activities were produced by the strain Steptomyces sp. GESEQ-4 DSM 106287. Our results provide new perspectives into the enzymatic array by which the Actinobacteria break down complex lignocellulosic biomass. It is crucial to assess the genome to determine enzyme function as well as the enzyme families responsible for the degradation process in Actinobacteria. The potential degradation functions for these actinobacterial strains were validated by testing their cellulolytic and hemicellulolytic activities with various lignocellulosic substrates.

Graphic Abstract

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Ahmed, A.A.Q., Babalola, O.O., McKay, T.: Cellulase- and xylanase-producing bacterial isolates with the ability to saccharify wheat straw and their potential use in the production of pharmaceuticals and chemicals from lignocellulosic materials. Waste Biomass Valorization 9, 765–775 (2018). https://doi.org/10.1007/s12649-017-9849-5

    Article  Google Scholar 

  2. 2.

    Leo, V. V., Asem, D., Zothanpuia, Singh, B.P.: Actinobacteria: A highly potent source for holocellulose degrading enzymes. In: Singh, B.P., Gupta, V.K., Passari, A.K. (eds.) New and Future Developments in Microbial Biotechnology and Bioengineering: Actinobacteria: Diversity and Biotechnological Applications, pp. 191–205. Elsevier, Amsterdam (2018)

  3. 3.

    Větrovský, T., Steffen, K.T., Baldrian, P.: Potential of cometabolic transformation of polysaccharides and lignin in lignocellulose by soil Actinobacteria. PLoS ONE 9, e89108 (2014). https://doi.org/10.1371/journal.pone.0089108

    Article  Google Scholar 

  4. 4.

    López-Mondéjar, R., Algora, C., Baldrian, P.: Lignocellulolytic systems of soil bacteria: a vast and diverse toolbox for biotechnological conversion processes. Biotechnol. Adv. 37(6), 107374 (2019)

    Article  Google Scholar 

  5. 5.

    Kameshwar, S., Qin, W.: Recent developments in using advanced sequencing technologies for the genomic studies of lignin and cellulose degrading microorganisms. Int J Biol Sci. 12(2), 156 (2016). https://doi.org/10.7150/ijbs.13537

    Article  Google Scholar 

  6. 6.

    Anteneh, Y.S., Franco, C.M.M.: Whole cell actinobacteria as biocatalysts, www.frontiersin.org, (2019). https://doi.org/10.3389/fmicb.2019.00077

  7. 7.

    Blackman, L.M., Cullerne, D.P., Hardham, A.R.: Bioinformatic characterisation of genes encoding cell wall degrading enzymes in the Phytophthora parasitica genome. BMC Genomics (2014). https://doi.org/10.1186/1471-2164-15-785

    Article  Google Scholar 

  8. 8.

    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P.M., Henrissat, B.: The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. (2014). https://doi.org/10.1093/nar/gkt1178

    Article  Google Scholar 

  9. 9.

    Anderson, I., Abt, B., Lykidis, A., Klenk, H.-P., Kyrpides, N., Ivanova, N.: Genomics of aerobic cellulose utilization systems in Actinobacteria. PLoS ONE 7, e39331 (2012). https://doi.org/10.1371/journal.pone.0039331

    Article  Google Scholar 

  10. 10.

    Cui, J., Mai, G., Wang, Z., Liu, Q., Zhou, Y., Ma, Y., Liu, C.: Metagenomic insights into a cellulose-rich niche reveal microbial cooperation in cellulose degradation. Front. Microbiol. (2019). https://doi.org/10.3389/fmicb.2019.00618

    Article  Google Scholar 

  11. 11.

    Küster, E., Williams, S.T.: Selection of media for isolation of streptomycetes. Nature 202, 928–929 (1964). https://doi.org/10.1038/202928a0

    Article  Google Scholar 

  12. 12.

    Hayakawa, M., Nonomura, H.: Humic acid-vitamin agar, a new medium for the selective isolation of soil actinomycetes. J. Ferment. Technol. 65, 501–509 (1987). https://doi.org/10.1016/0385-6380(87)90108-7

    Article  Google Scholar 

  13. 13.

    Houfani, A.A., Větrovský, T., Baldrian, P., Benallaoua, S.: Efficient screening of potential cellulases and hemicellulases produced by Bosea sp. FBZP-16 using the combination of enzyme assays and genome analysis. World J. Microbiol. Biotechnol. 33, 1–14 (2017). https://doi.org/10.1007/s11274-016-2198-x

    Article  Google Scholar 

  14. 14.

    Baldrian, P.: Microbial enzyme-catalyzed processes in soils and their analysis. Plant Soil Environ. 55, 370–378 (2009). https://doi.org/10.1007/s11104-008-9731-0

    Article  Google Scholar 

  15. 15.

    Lane, D.J.: 16S/23S rRNA sequencing. In: Stackebrandt, E.G., Goodfellow, M. (eds.) Nucleic Acid Techniques in Bacterial Systematics, pp. 115–175. Wiley, New York (1991)

    Google Scholar 

  16. 16.

    Zerbino, D.R., Birney, E.: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 18, 821–829 (2008). https://doi.org/10.1101/gr.074492.107

    Article  Google Scholar 

  17. 17.

    Bankevich, A., Nurk, S., Antipov, D., Gurevich, A.A., Dvorkin, M., Kulikov, A.S., Lesin, V.M., Nikolenko, S.I., Pham, S.O.N., Prjibelski, A.D., Pyshkin, A. V, Sirotkin, A. V, Vyahhi, N., Tesler, G., Alekseyev, M.A.X.A., Pevzner, P.A.: SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012). https://doi.org/10.1089/cmb.2012.0021

  18. 18.

    Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., Edwards, R.: The metagenomics RAST server: a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 9, 1–8 (2008). https://doi.org/10.1002/9781118010518.ch37

    Article  Google Scholar 

  19. 19.

    Yin, Y., Mao, X., Yang, J., Chen, X., Mao, F., Xu, Y.: DbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, 445–451 (2012). https://doi.org/10.1093/nar/gks479

    Article  Google Scholar 

  20. 20.

    Seemann, T.: Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014). https://doi.org/10.1093/bioinformatics/btu153

    Article  Google Scholar 

  21. 21.

    Lee, M.D.: GToTree: a user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019). https://doi.org/10.1093/bioinformatics/btz188

    Article  Google Scholar 

  22. 22.

    Hyatt, D., Chen, G.L., LoCascio, P.F., Land, M.L., Larimer, F.W., Hauser, L.J.: Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 11, 119 (2010). https://doi.org/10.1186/1471-2105-11-119

    Article  Google Scholar 

  23. 23.

    Eddy, S.R.: Accelerated profile HMM searches. PLoS Comput. Biol. 7, 1002195 (2011). https://doi.org/10.1371/journal.pcbi.1002195

    MathSciNet  Article  Google Scholar 

  24. 24.

    Edgar, R.C.: MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004). https://doi.org/10.1093/nar/gkh340

    Article  Google Scholar 

  25. 25.

    Capella-Gutierrez, S., Silla-Martinez, J.M., Gabaldon, T.: trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009). https://doi.org/10.1093/bioinformatics/btp348

    Article  Google Scholar 

  26. 26.

    Price, M.N., Dehal, P.S., Arkin, A.P.: FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010). https://doi.org/10.1371/journal.pone.0009490

    Article  Google Scholar 

  27. 27.

    Tange, O.: GNU Parallel 2018. (2018). https://doi.org/10.5281/ZENODO.1146014

  28. 28.

    Letunic, I., Bork, P.: Interactive Tree of Life v2: online annotation and display of phylogenetic trees made easy. Nucleic Acids Res. 39, W475–W478 (2011). https://doi.org/10.1093/nar/gkr201

    Article  Google Scholar 

  29. 29.

    Lacombe-Harvey, M.-È., Brzezinski, R., Beaulieu, C.: Chitinolytic functions in actinobacteria: ecology, enzymes, and evolution. Appl. Microbiol. Biotechnol. 102, 7219–7230 (2018). https://doi.org/10.1007/s00253-018-9149-4

    Article  Google Scholar 

  30. 30.

    Sharma, H.K., Xu, C., Qin, W.: Biological Pretreatment of Lignocellulosic Biomass for Biofuels and Bioproducts: An Overview. https://www.bp.com/ (2019)

  31. 31.

    Riyadi, F.A., Tahir, A.A., Yusof, N., Sabri, N.S.A., Noor, M.J.M.M., Akhir, F.N.M.D., Othman, N., Zakaria, Z., Hara, H.: Enzymatic and genetic characterization of lignin depolymerization by Streptomyces sp. S6 isolated from a tropical environment. Sci. Rep. 10, 1–9 (2020). https://doi.org/10.1038/s41598-020-64817-4

    Article  Google Scholar 

  32. 32.

    Shrestha, S., Kognou, A.L.M., Zhang, J., Qin, W.: Different facets of lignocellulosic biomass including pectin and its perspectives. Waste Biomass Valorization 12, 4805–4823 (2020). https://doi.org/10.1007/s12649-020-01305-w

    Article  Google Scholar 

  33. 33.

    Aakko, J., Pietilä, S., Toivonen, R., Rokka, A., Mokkala, K., Laitinen, K., Elo, L., Hänninen, A.: A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota. Sci. Rep. 10, 12411 (2020). https://doi.org/10.1038/s41598-020-69241-2

    Article  Google Scholar 

  34. 34.

    Boutard, M., Cerisy, T., Nogue, P.-Y., Alberti, A., Weissenbach, J., Salanoubat, M., Tolonen, A.C.: Functional diversity of carbohydrate-active enzymes enabling a bacterium to ferment plant biomass. PLoS Genet. 10, e1004773 (2014). https://doi.org/10.1371/journal.pgen.1004773

    Article  Google Scholar 

  35. 35.

    Lladó Fernández, S., Větrovský, T., Baldrian, P.: The concept of operational taxonomic units revisited: genomes of bacteria that are regarded as closely related are often highly dissimilar. Folia Microbiol. (Praha) 64, 19–23 (2019). https://doi.org/10.1007/s12223-018-0627-y

    Article  Google Scholar 

  36. 36.

    Berlemont, R., Martiny, A.C.: Genomic potential for polysaccharide deconstruction in bacteria. Appl. Environ. Microbiol. 81, 1513–1519 (2015). https://doi.org/10.1128/AEM.03718-14

    Article  Google Scholar 

  37. 37.

    Berlemont, R., Martiny, A.C.: Phylogenetic distribution of potential cellulases in bacteria. Appl. Environ. Microbiol. 79, 1545–1554 (2013). https://doi.org/10.1128/AEM.03305-12

    Article  Google Scholar 

  38. 38.

    Ventura, M., Canchaya, C., Tauch, A., Chandra, G., Fitzgerald, G.F., Chater, K.F., van Sinderen, D.: Genomics of Actinobacteria: tracing the evolutionary history of an ancient phylum. Microbiol. Mol. Biol. Rev. 71, 495–548 (2007). https://doi.org/10.1128/mmbr.00005-07

    Article  Google Scholar 

  39. 39.

    Malik, A., Kim, Y.R., Jang, I.H., Hwang, S., Oh, D.C., Kim, S.B.: Genome-based analysis for the bioactive potential of Streptomyces yeochonensis CN732, an acidophilic filamentous soil actinobacterium. BMC Genomics 21, 118 (2020). https://doi.org/10.1186/s12864-020-6468-5

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support by the Algerian Ministry of Higher Education and Scientific Research and the General Direction for Scientific Research and Technological Development (Algeria). The authors acknowledge Lamia Medouni–Haroune (Laboratoire de Microbiologie Appliquée, Université de Bejaia) and Karel Švec (Laboratory of Fungal Genetics and Metabolism, Institute of Microbiology of the Czech Academy of Sciences) for providing olive pomace and oat flakes, respectively.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Aicha Asma Houfani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 18 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Houfani, A.A., Tláskal, V., Baldrian, P. et al. Actinobacterial Strains as Genomic Candidates for Characterization of Genes Encoding Enzymes in Bioconversion of Lignocellulose. Waste Biomass Valor (2021). https://doi.org/10.1007/s12649-021-01595-8

Download citation

Keywords

  • Lignocellulosic biomass
  • CAZymes
  • Cellulases
  • Hemicellulases
  • Actinobacteria
  • Genome sequencing